Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources

Khan, Inam Ullah and Javaid, Nadeem and Akurugoda Gamage, Kelum and Taylor, C. James and Baig, Sobia and Ma, Xiandong (2020) Heuristic algorithm based optimal power flow model incorporating stochastic renewable energy sources. IEEE Access, 8. pp. 148622-148643. ISSN 2169-3536

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Abstract

Today's electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWO-based approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Access
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2500
Subjects:
ID Code:
146458
Deposited By:
Deposited On:
10 Aug 2020 15:30
Refereed?:
Yes
Published?:
Published
Last Modified:
21 Oct 2020 07:16